Computer-controlled object detection from digital images and tracking them in a video sequence are extremely challenging tasks. Nevertheless, it has become a very popular research topic due to a wide range of applications available for it. One of the main application areas in the technology field is face recognition or detection, which is widely used, for example, in security systems. Furthermore, it enables interesting features, such as automatic red eye removal, enhancement of the face area resolution, automatic focus of a camera and interpretation of the identity of a person.
However, current solutions for performing face detection include several disadvantages. Typically, in the face detection, a few facial features are extracted from the image and compared to a predetermined feature database. Current face detection technologies apply, for example, simple Haar-wavelet features selected from an integral image constructed from an original grayscale image. Another technique addresses local binary patterns (LBP), where information is obtained by comparing one location of an image (one pixel) to one neighboring pixel. Both of these techniques have disadvantages related to, for example, lack of discriminating facial features leading to poor accuracy, excessive amount of features and data leading to slow processing, poor consideration of a local structure of neighboring pixels, uneven illumination of the face, and varying viewpoint towards the face.
Consequently, a novel solution for performing the object detection and tracking in digital images is needed.